TY - JOUR A1 - Piro, Vitor C. A1 - Renard, Bernhard Y. T1 - Contamination detection and microbiome exploration with GRIMER JF - GigaScience N2 - Background: Contamination detection is a important step that should be carefully considered in early stages when designing and performing microbiome studies to avoid biased outcomes. Detecting and removing true contaminants is challenging, especially in low-biomass samples or in studies lacking proper controls. Interactive visualizations and analysis platforms are crucial to better guide this step, to help to identify and detect noisy patterns that could potentially be contamination. Additionally, external evidence, like aggregation of several contamination detection methods and the use of common contaminants reported in the literature, could help to discover and mitigate contamination. Results: We propose GRIMER, a tool that performs automated analyses and generates a portable and interactive dashboard integrating annotation, taxonomy, and metadata. It unifies several sources of evidence to help detect contamination. GRIMER is independent of quantification methods and directly analyzes contingency tables to create an interactive and offline report. Reports can be created in seconds and are accessible for nonspecialists, providing an intuitive set of charts to explore data distribution among observations and samples and its connections with external sources. Further, we compiled and used an extensive list of possible external contaminant taxa and common contaminants with 210 genera and 627 species reported in 22 published articles. Conclusion: GRIMER enables visual data exploration and analysis, supporting contamination detection in microbiome studies. The tool and data presented are open source and available at https://gitlab.com/dacs-hpi/grimer. KW - Contamination KW - Microbiome KW - Visualization KW - Taxonomy Y1 - 2023 U6 - https://doi.org/10.1093/gigascience/giad017 SN - 2047-217X VL - 12 PB - Oxford Univ. Press CY - Oxford ER - TY - JOUR A1 - Gärtner, Thomas A1 - Schneider, Juliana A1 - Arnrich, Bert A1 - Konigorski, Stefan T1 - Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects JF - BMC Medical Research Methodology N2 - Background The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there is carry-over or complex dependencies of the treatment effect of interest. Methods In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. For this, we simulate data of a series of N-of-1 trials for Chronic Nonspecific Low Back Pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks, and G-estimation, we introduce a carry-over adjusted parametric model (COAPM). Results The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase. Conclusions This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios. KW - N-of-1 trials KW - Randomized clinical trials KW - Bayesian Networks; KW - G-estimation KW - Linear model KW - Simulation study KW - Chronic Nonspecific Low KW - Back Pain Y1 - 2023 U6 - https://doi.org/10.1186/s12874-023-02012-5 SN - 1471-2288 VL - 23 IS - 1 PB - BMC CY - London ER - TY - JOUR A1 - Lewkowicz, Daniel A1 - Böttinger, Erwin A1 - Siegel, Martin T1 - Economic evaluation of digital therapeutic care apps for unsupervised treatment of low back pain BT - Monte Carlo Simulation JF - JMIR mhealth and uhealth N2 - Background: Digital therapeutic care (DTC) programs are unsupervised app-based treatments that provide video exercises and educational material to patients with nonspecific low back pain during episodes of pain and functional disability. German statutory health insurance can reimburse DTC programs since 2019, but evidence on efficacy and reasonable pricing remains scarce. This paper presents a probabilistic sensitivity analysis (PSA) to evaluate the efficacy and cost-utility of a DTC app against treatment as usual (TAU) in Germany. Objective: The aim of this study was to perform a PSA in the form of a Monte Carlo simulation based on the deterministic base case analysis to account for model assumptions and parameter uncertainty. We also intend to explore to what extent the results in this probabilistic analysis differ from the results in the base case analysis and to what extent a shortage of outcome data concerning quality-of-life (QoL) metrics impacts the overall results. Methods: The PSA builds upon a state-transition Markov chain with a 4-week cycle length over a model time horizon of 3 years from a recently published deterministic cost-utility analysis. A Monte Carlo simulation with 10,000 iterations and a cohort size of 10,000 was employed to evaluate the cost-utility from a societal perspective. Quality-adjusted life years (QALYs) were derived from Veterans RAND 6-Dimension (VR-6D) and Short-Form 6-Dimension (SF-6D) single utility scores. Finally, we also simulated reducing the price for a 3-month app prescription to analyze at which price threshold DTC would result in being the dominant strategy over TAU in Germany. Results: The Monte Carlo simulation yielded on average a euro135.97 (a currency exchange rate of EUR euro1=US $1.069 is applicable) incremental cost and 0.004 incremental QALYs per person and year for the unsupervised DTC app strategy compared to in-person physiotherapy in Germany. The corresponding incremental cost-utility ratio (ICUR) amounts to an additional euro34,315.19 per additional QALY. DTC yielded more QALYs in 54.96% of the iterations. DTC dominates TAU in 24.04% of the iterations for QALYs. Reducing the app price in the simulation from currently euro239.96 to euro164.61 for a 3-month prescription could yield a negative ICUR and thus make DTC the dominant strategy, even though the estimated probability of DTC being more effective than TAU is only 54.96%. Conclusions: Decision-makers should be cautious when considering the reimbursement of DTC apps since no significant treatment effect was found, and the probability of cost-effectiveness remains below 60% even for an infinite willingness-to-pay threshold. More app-based studies involving the utilization of QoL outcome parameters are urgently needed to account for the low and limited precision of the available QoL input parameters, which are crucial to making profound recommendations concerning the cost-utility of novel apps. KW - cost-utility analysis KW - cost KW - probabilistic sensitivity analysis KW - Monte Carlo simulation KW - low back pain KW - pain KW - economic KW - cost-effectiveness KW - Markov model KW - digital therapy KW - digital health app KW - mHealth KW - mobile health KW - health app KW - mobile app KW - orthopedic KW - QUALY KW - DALY KW - quality-adjusted life years KW - disability-adjusted life years KW - time horizon KW - veteran KW - statistics Y1 - 2023 U6 - https://doi.org/10.2196/44585 SN - 2291-5222 VL - 11 PB - JMIR Publications CY - Toronto ER - TY - JOUR A1 - Moontaha, Sidratul A1 - Schumann, Franziska Elisabeth Friederike A1 - Arnrich, Bert T1 - Online learning for wearable EEG-Based emotion classification JF - Sensors N2 - Giving emotional intelligence to machines can facilitate the early detection and prediction of mental diseases and symptoms. Electroencephalography (EEG)-based emotion recognition is widely applied because it measures electrical correlates directly from the brain rather than indirect measurement of other physiological responses initiated by the brain. Therefore, we used non-invasive and portable EEG sensors to develop a real-time emotion classification pipeline. The pipeline trains different binary classifiers for Valence and Arousal dimensions from an incoming EEG data stream achieving a 23.9% (Arousal) and 25.8% (Valence) higher F1-Score on the state-of-art AMIGOS dataset than previous work. Afterward, the pipeline was applied to the curated dataset from 15 participants using two consumer-grade EEG devices while watching 16 short emotional videos in a controlled environment. Mean F1-Scores of 87% (Arousal) and 82% (Valence) were achieved for an immediate label setting. Additionally, the pipeline proved to be fast enough to achieve predictions in real-time in a live scenario with delayed labels while continuously being updated. The significant discrepancy from the readily available labels on the classification scores leads to future work to include more data. Thereafter, the pipeline is ready to be used for real-time applications of emotion classification. KW - online learning KW - real-time KW - emotion classification KW - AMIGOS dataset KW - wearable EEG (muse and neurosity crown) KW - psychopy experiments Y1 - 2023 U6 - https://doi.org/10.3390/s23052387 SN - 1424-8220 VL - 23 IS - 5 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kirchler, Matthias A1 - Konigorski, Stefan A1 - Norden, Matthias A1 - Meltendorf, Christian A1 - Kloft, Marius A1 - Schurmann, Claudia A1 - Lippert, Christoph T1 - transferGWAS BT - GWAS of images using deep transfer learning JF - Bioinformatics N2 - Motivation: Medical images can provide rich information about diseases and their biology. However, investigating their association with genetic variation requires non-standard methods. We propose transferGWAS, a novel approach to perform genome-wide association studies directly on full medical images. First, we learn semantically meaningful representations of the images based on a transfer learning task, during which a deep neural network is trained on independent but similar data. Then, we perform genetic association tests with these representations. Results: We validate the type I error rates and power of transferGWAS in simulation studies of synthetic images. Then we apply transferGWAS in a genome-wide association study of retinal fundus images from the UK Biobank. This first-of-a-kind GWAS of full imaging data yielded 60 genomic regions associated with retinal fundus images, of which 7 are novel candidate loci for eye-related traits and diseases. Y1 - 2022 U6 - https://doi.org/10.1093/bioinformatics/btac369 SN - 1367-4803 SN - 1460-2059 VL - 38 IS - 14 SP - 3621 EP - 3628 PB - Oxford Univ. Press CY - Oxford ER - TY - THES A1 - Lorson, Annalena T1 - Understanding early stage evolution of digital innovation units in manufacturing companies T1 - Verständnis der frühphasigen Entwicklung digitaler Innovationseinheiten in Fertigungsunternehmen N2 - The dynamic landscape of digital transformation entails an impact on industrial-age manufacturing companies that goes beyond product offerings, changing operational paradigms, and requiring an organization-wide metamorphosis. An initiative to address the given challenges is the creation of Digital Innovation Units (DIUs) – departments or distinct legal entities that use new structures and practices to develop digital products, services, and business models and support or drive incumbents’ digital transformation. With more than 300 units in German-speaking countries alone and an increasing number of scientific publications, DIUs have become a widespread phenomenon in both research and practice. This dissertation examines the evolution process of DIUs in the manufacturing industry during their first three years of operation, through an extensive longitudinal single-case study and several cross-case syntheses of seven DIUs. Building on the lenses of organizational change and development, time, and socio-technical systems, this research provides insights into the fundamentals, temporal dynamics, socio-technical interactions, and relational dynamics of a DIU’s evolution process. Thus, the dissertation promotes a dynamic understanding of DIUs and adds a two-dimensional perspective to the often one-dimensional view of these units and their interactions with the main organization throughout the startup and growth phases of a DIU. Furthermore, the dissertation constructs a phase model that depicts the early stages of DIU evolution based on these findings and by incorporating literature from information systems research. As a result, it illustrates the progressive intensification of collaboration between the DIU and the main organization. After being implemented, the DIU sparks initial collaboration and instigates change within (parts of) the main organization. Over time, it adapts to the corporate environment to some extent, responding to changing circumstances in order to contribute to long-term transformation. Temporally, the DIU drives the early phases of cooperation and adaptation in particular, while the main organization triggers the first major evolutionary step and realignment of the DIU. Overall, the thesis identifies DIUs as malleable organizational structures that are crucial for digital transformation. Moreover, it provides guidance for practitioners on the process of building a new DIU from scratch or optimizing an existing one. N2 - Die digitale Transformation produzierender Unternehmen geht über die bloße Veränderung des Produktangebots hinaus; sie durchdringt operative Paradigmen und erfordert eine umfassende, unternehmensweite Metamorphose. Eine Initiative, den damit verbundenen Herausforderungen zu begegnen, ist der Aufbau einer Digital Innovation Unit (DIU) (zu deutsch: digitale Innovationseinheit) – eine Abteilung oder separate rechtliche Einheit, die neue organisationale Strukturen und Arbeitspraktiken nutzt, um digitale Produkte, Dienstleistungen und Geschäftsmodelle zu entwickeln und die digitale Transformation von etabliertenUnternehmen zu unterstützen oder voranzutreiben. Mit mehr als 300 Einheitenallein im deutschsprachigen Raum und einer wachsenden Zahl wissenschaftlicher Publikationen sind DIUs sowohl in der Forschung als auch in der Praxis ein weit verbreitetes Phänomen. Auf Basis einer umfassenden Längsschnittstudie und mehrerer Querschnittsanalysen von sieben Fertigungsunternehmen und ihren DIUs untersucht diese Dissertation den Entwicklungsprozess von DIUs in den ersten drei Betriebsjahren. Gestützt auf theoretische Perspektiven zu organisatorischem Wandel, Zeit und sozio-technischen Systemen bietet sie Einblicke in die Grundlagen, die zeitlichen Dynamiken, die sozio-technischen Interaktionen und die Beziehungsdynamiken des Entwicklungsprozesses von DIUs. Die Dissertation erweitert somit das dynamische Verständnis von DIUs und fügt der oft eindimensionalen Sichtweise auf diese Einheiten und ihre Interaktionen mit der Hauptorganisation eine zweidimensionale Perspektive entlang der Gründungs- und Wachstumsphasen einer DIU hinzu. Darüber hinaus konstruiert die Dissertation ein Phasenmodell, das die frühen Phasen der DIU-Entwicklung auf der Grundlage dieser Erkenntnisse und unter Einbeziehung von Literatur aus der Wirtschaftsinformatikforschung abbildet. Es veranschaulicht die schrittweise Intensivierung der Zusammenarbeit zwischen der DIU und der Hauptorganisation. Nach ihrer Implementierung initiiert die DIU die anfängliche Zusammenarbeit und stößt Veränderungen innerhalb (von Teilen) der Hauptorganisation an. Im Laufe der Zeit passt sich die DIU bis zu einem gewissen Grad dem Unternehmensumfeld an und reagiert auf sich verändernde Umstände, um zu einer langfristigen Veränderung beizutragen. Zeitlich gesehen treibt die DIU vor allem die frühen Phasen der Zusammenarbeit und Anpassung voran, während die Hauptorganisation den ersten großen Entwicklungsschritt und die Neuausrichtung der DIU auslöst. Insgesamt identifiziert die Dissertation DIUs als anpassungsfähige Organisationsstrukturen, die für die digitale Transformation entscheidend sind. Darüber hinaus bietet sie Praktikern einen Leitfaden für den Aufbau einer neuen oder die Optimierung einer bestehenden DIU. KW - digital transformation KW - digital innovation units KW - evolution of digital innovation units KW - manufacturing companies KW - digitale Transformation KW - digitale Innovationseinheit KW - Entwicklung digitaler Innovationseinheiten KW - Fertigungsunternehmen Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-639141 ER - TY - JOUR A1 - Cseh, Agnes A1 - Faenza, Yuri A1 - Kavitha, Telikepalli A1 - Powers, Vladlena T1 - Understanding popular matchings via stable matchings JF - SIAM journal on discrete mathematics N2 - An instance of the marriage problem is given by a graph G = (A boolean OR B, E), together with, for each vertex of G, a strict preference order over its neighbors. A matching M of G is popular in the marriage instance if M does not lose a head-to-head election against any matching where vertices are voters. Every stable matching is a min-size popular matching; another subclass of popular matchings that always exists and can be easily computed is the set of dominant matchings. A popular matching M is dominant if M wins the head-to-head election against any larger matching. Thus, every dominant matching is a max-size popular matching, and it is known that the set of dominant matchings is the linear image of the set of stable matchings in an auxiliary graph. Results from the literature seem to suggest that stable and dominant matchings behave, from a complexity theory point of view, in a very similar manner within the class of popular matchings. The goal of this paper is to show that there are instead differences in the tractability of stable and dominant matchings and to investigate further their importance for popular matchings. First, we show that it is easy to check if all popular matchings are also stable; however, it is co-NP hard to check if all popular matchings are also dominant. Second, we show how some new and recent hardness results on popular matching problems can be deduced from the NP-hardness of certain problems on stable matchings, also studied in this paper, thus showing that stable matchings can be employed to show not only positive results on popular matchings (as is known) but also most negative ones. Problems for which we show new hardness results include finding a min-size (resp., max-size) popular matching that is not stable (resp., dominant). A known result for which we give a new and simple proof is the NP-hardness of finding a popular matching when G is nonbipartite. KW - popular matching KW - stable matching KW - complexity KW - dominant matching Y1 - 2022 U6 - https://doi.org/10.1137/19M124770X SN - 0895-4801 SN - 1095-7146 VL - 36 IS - 1 SP - 188 EP - 213 PB - Society for Industrial and Applied Mathematics CY - Philadelphia ER - TY - THES A1 - Huegle, Johannes T1 - Causal discovery in practice: Non-parametric conditional independence testing and tooling for causal discovery T1 - Kausale Entdeckung in der Praxis: Nichtparametrische bedingte Unabhängigkeitstests und Werkzeuge für die Kausalentdeckung N2 - Knowledge about causal structures is crucial for decision support in various domains. For example, in discrete manufacturing, identifying the root causes of failures and quality deviations that interrupt the highly automated production process requires causal structural knowledge. However, in practice, root cause analysis is usually built upon individual expert knowledge about associative relationships. But, "correlation does not imply causation", and misinterpreting associations often leads to incorrect conclusions. Recent developments in methods for causal discovery from observational data have opened the opportunity for a data-driven examination. Despite its potential for data-driven decision support, omnipresent challenges impede causal discovery in real-world scenarios. In this thesis, we make a threefold contribution to improving causal discovery in practice. (1) The growing interest in causal discovery has led to a broad spectrum of methods with specific assumptions on the data and various implementations. Hence, application in practice requires careful consideration of existing methods, which becomes laborious when dealing with various parameters, assumptions, and implementations in different programming languages. Additionally, evaluation is challenging due to the lack of ground truth in practice and limited benchmark data that reflect real-world data characteristics. To address these issues, we present a platform-independent modular pipeline for causal discovery and a ground truth framework for synthetic data generation that provides comprehensive evaluation opportunities, e.g., to examine the accuracy of causal discovery methods in case of inappropriate assumptions. (2) Applying constraint-based methods for causal discovery requires selecting a conditional independence (CI) test, which is particularly challenging in mixed discrete-continuous data omnipresent in many real-world scenarios. In this context, inappropriate assumptions on the data or the commonly applied discretization of continuous variables reduce the accuracy of CI decisions, leading to incorrect causal structures. Therefore, we contribute a non-parametric CI test leveraging k-nearest neighbors methods and prove its statistical validity and power in mixed discrete-continuous data, as well as the asymptotic consistency when used in constraint-based causal discovery. An extensive evaluation of synthetic and real-world data shows that the proposed CI test outperforms state-of-the-art approaches in the accuracy of CI testing and causal discovery, particularly in settings with low sample sizes. (3) To show the applicability and opportunities of causal discovery in practice, we examine our contributions in real-world discrete manufacturing use cases. For example, we showcase how causal structural knowledge helps to understand unforeseen production downtimes or adds decision support in case of failures and quality deviations in automotive body shop assembly lines. N2 - Kenntnisse über die Strukturen zugrundeliegender kausaler Mechanismen sind eine Voraussetzung für die Entscheidungsunterstützung in verschiedenen Bereichen. In der Fertigungsindustrie beispielsweise erfordert die Fehler-Ursachen-Analyse von Störungen und Qualitätsabweichungen, die den hochautomatisierten Produktionsprozess unterbrechen, kausales Strukturwissen. In Praxis stützt sich die Fehler-Ursachen-Analyse in der Regel jedoch auf individuellem Expertenwissen über assoziative Zusammenhänge. Aber "Korrelation impliziert nicht Kausalität", und die Fehlinterpretation assoziativer Zusammenhänge führt häufig zu falschen Schlussfolgerungen. Neueste Entwicklungen von Methoden des kausalen Strukturlernens haben die Möglichkeit einer datenbasierten Betrachtung eröffnet. Trotz seines Potenzials zur datenbasierten Entscheidungsunterstützung wird das kausale Strukturlernen in der Praxis jedoch durch allgegenwärtige Herausforderungen erschwert. In dieser Dissertation leisten wir einen dreifachen Beitrag zur Verbesserung des kausalen Strukturlernens in der Praxis. (1) Das wachsende Interesse an kausalem Strukturlernen hat zu einer Vielzahl von Methoden mit spezifischen statistischen Annahmen über die Daten und verschiedenen Implementierungen geführt. Daher erfordert die Anwendung in der Praxis eine sorgfältige Prüfung der vorhandenen Methoden, was eine Herausforderung darstellt, wenn verschiedene Parameter, Annahmen und Implementierungen in unterschiedlichen Programmiersprachen betrachtet werden. Hierbei wird die Evaluierung von Methoden des kausalen Strukturlernens zusätzlich durch das Fehlen von "Ground Truth" in der Praxis und begrenzten Benchmark-Daten, welche die Eigenschaften realer Datencharakteristiken widerspiegeln, erschwert. Um diese Probleme zu adressieren, stellen wir eine plattformunabhängige modulare Pipeline für kausales Strukturlernen und ein Tool zur Generierung synthetischer Daten vor, die umfassende Evaluierungsmöglichkeiten bieten, z.B. um Ungenauigkeiten von Methoden des Lernens kausaler Strukturen bei falschen Annahmen an die Daten aufzuzeigen. (2) Die Anwendung von constraint-basierten Methoden des kausalen Strukturlernens erfordert die Wahl eines bedingten Unabhängigkeitstests (CI-Test), was insbesondere bei gemischten diskreten und kontinuierlichen Daten, die in vielen realen Szenarien allgegenwärtig sind, die Anwendung erschwert. Beispielsweise führen falsche Annahmen der CI-Tests oder die Diskretisierung kontinuierlicher Variablen zu einer Verschlechterung der Korrektheit der Testentscheidungen, was in fehlerhaften kausalen Strukturen resultiert. Um diese Probleme zu adressieren, stellen wir einen nicht-parametrischen CI-Test vor, der auf Nächste-Nachbar-Methoden basiert, und beweisen dessen statistische Validität und Trennschärfe bei gemischten diskreten und kontinuierlichen Daten, sowie dessen asymptotische Konsistenz in constraint-basiertem kausalem Strukturlernen. Eine umfangreiche Evaluation auf synthetischen und realen Daten zeigt, dass der vorgeschlagene CI-Test bestehende Verfahren hinsichtlich der Korrektheit der Testentscheidung und gelernter kausaler Strukturen übertrifft, insbesondere bei geringen Stichprobengrößen. (3) Um die Anwendbarkeit und Möglichkeiten kausalen Strukturlernens in der Praxis aufzuzeigen, untersuchen wir unsere Beiträge in realen Anwendungsfällen aus der Fertigungsindustrie. Wir zeigen an mehreren Beispielen aus der automobilen Karosseriefertigungen wie kausales Strukturwissen helfen kann, unvorhergesehene Produktionsausfälle zu verstehen oder eine Entscheidungsunterstützung bei Störungen und Qualitätsabweichungen zu geben. KW - causal discovery KW - causal structure learning KW - causal AI KW - non-parametric conditional independence testing KW - manufacturing KW - causal reasoning KW - mixed data KW - kausale KI KW - kausale Entdeckung KW - kausale Schlussfolgerung KW - kausales Strukturlernen KW - Fertigung KW - gemischte Daten KW - nicht-parametrische bedingte Unabhängigkeitstests Y1 - 2024 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-635820 ER - TY - JOUR A1 - Casel, Katrin A1 - Fernau, Henning A1 - Ghadikolaei, Mehdi Khosravian A1 - Monnot, Jerome A1 - Sikora, Florian T1 - On the complexity of solution extension of optimization problems JF - Theoretical computer science : the journal of the EATCS N2 - The question if a given partial solution to a problem can be extended reasonably occurs in many algorithmic approaches for optimization problems. For instance, when enumerating minimal vertex covers of a graph G = (V, E), one usually arrives at the problem to decide for a vertex set U subset of V (pre-solution), if there exists a minimal vertex cover S (i.e., a vertex cover S subset of V such that no proper subset of S is a vertex cover) with U subset of S (minimal extension of U). We propose a general, partial-order based formulation of such extension problems which allows to model parameterization and approximation aspects of extension, and also highlights relationships between extension tasks for different specific problems. As examples, we study a number of specific problems which can be expressed and related in this framework. In particular, we discuss extension variants of the problems dominating set and feedback vertex/edge set. All these problems are shown to be NP-complete even when restricted to bipartite graphs of bounded degree, with the exception of our extension version of feedback edge set on undirected graphs which is shown to be solvable in polynomial time. For the extension variants of dominating and feedback vertex set, we also show NP-completeness for the restriction to planar graphs of bounded degree. As non-graph problem, we also study an extension version of the bin packing problem. We further consider the parameterized complexity of all these extension variants, where the parameter is a measure of the pre-solution as defined by our framework. KW - extension problems KW - NP-hardness KW - parameterized complexity Y1 - 2022 U6 - https://doi.org/10.1016/j.tcs.2021.10.017 SN - 0304-3975 SN - 1879-2294 VL - 904 SP - 48 EP - 65 PB - Elsevier CY - Amsterdam [u.a.] ER - TY - JOUR A1 - Coupette, Corinna A1 - Hartung, Dirk A1 - Beckedorf, Janis A1 - Böther, Maximilian A1 - Katz, Daniel Martin T1 - Law smells BT - defining and detecting problematic patterns in legal drafting JF - Artificial intelligence and law N2 - Building on the computer science concept of code smells, we initiate the study of law smells, i.e., patterns in legal texts that pose threats to the comprehensibility and maintainability of the law. With five intuitive law smells as running examples-namely, duplicated phrase, long element, large reference tree, ambiguous syntax, and natural language obsession-, we develop a comprehensive law smell taxonomy. This taxonomy classifies law smells by when they can be detected, which aspects of law they relate to, and how they can be discovered. We introduce text-based and graph-based methods to identify instances of law smells, confirming their utility in practice using the United States Code as a test case. Our work demonstrates how ideas from software engineering can be leveraged to assess and improve the quality of legal code, thus drawing attention to an understudied area in the intersection of law and computer science and highlighting the potential of computational legal drafting. KW - Refactoring KW - Software engineering KW - Law KW - Natural language processing KW - Network analysis Y1 - 2022 U6 - https://doi.org/10.1007/s10506-022-09315-w SN - 0924-8463 SN - 1572-8382 VL - 31 SP - 335 EP - 368 PB - Springer CY - Dordrecht ER -